0.5%>100%: Bidirectional Reciprocal Learning for Referring Image Segmentation

arXiv:2609.24510 · cs.CV · Submitted 2026-09-21 · Read on arXiv

cs.CV

Submitted: 2026-09-21

Updated: 2026-09-22

Code: https://github.com/xiaoqiang-lu/BRL

Terminology

• Adaptation — 48× in this paper · explained in 1 episode
• Transformer — 37× in this paper · explained in 2 episodes
• Foundation Model — 30× in this paper · explained in 3 episodes
• Fine-Tuning — 28× in this paper · explained in 5 episodes
• Foundation Models — 24× in this paper · explained in 7 episodes
• Benchmark — 23× in this paper · explained in 2 episodes
• Alignment — 16× in this paper · explained in 1 episode
• Grounding — 15× in this paper · explained in 3 episodes
• Bidirectional Reciprocal Learning — 11× in this paper
• Parameter-Efficient Fine-Tuning — 10× in this paper · explained in 2 episodes
• Consistency — 8× in this paper · explained in 3 episodes
• Attention Mechanism — 6× in this paper · explained in 2 episodes
• Parameter-Efficient Fine-Tuning (PEFT) — 6× in this paper · explained in 4 episodes
• Reciprocal Attention Adapter — 6× in this paper
• Reciprocal Gate Adapter — 6× in this paper
• Visual Grounding — 6× in this paper · explained in 2 episodes
• Computational overhead — 5× in this paper · explained in 2 episodes
• Redundancy — 5× in this paper · explained in 2 episodes
• Representation Learning — 5× in this paper · explained in 2 episodes
• Robustness — 5× in this paper · explained in 5 episodes
• Ablation — 4× in this paper · explained in 1 episode
• Catastrophic Forgetting — 4× in this paper · explained in 5 episodes
• Core — 4× in this paper · explained in 2 episodes
• Cross-Attention Mechanism — 4× in this paper · explained in 2 episodes
• Aggregation — 3× in this paper · explained in 1 episode
• Generalization — 3× in this paper · explained in 13 episodes
• Instruction Tuning — 3× in this paper · explained in 2 episodes
• Ablation study — 2× in this paper · explained in 1 episode
• Accuracy — 2× in this paper · explained in 1 episode
• Activation Function — 2× in this paper · explained in 1 episode
• Adam — 2× in this paper · explained in 2 episodes
• Feature Selection — 2× in this paper · explained in 4 episodes
• Latent Space — 2× in this paper · explained in 7 episodes
• Multimodal Learning — 2× in this paper · explained in 2 episodes
• Multimodal Reasoning — 2× in this paper · explained in 5 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• Adaptive Selection — 1× in this paper · explained in 1 episode
• Clustering — 1× in this paper · explained in 2 episodes
• Contextual Understanding — 1× in this paper · explained in 2 episodes
• Data Augmentation — 1× in this paper · explained in 7 episodes
• Distillation — 1× in this paper · explained in 2 episodes
• Downstream Tasks — 1× in this paper · explained in 2 episodes
• Frozen model — 1× in this paper · explained in 3 episodes
• Interpretability — 1× in this paper · explained in 15 episodes
• Knowledge Distillation — 1× in this paper · explained in 11 episodes
• Parameter Efficiency — 1× in this paper · explained in 3 episodes
• Segmentation Masks — 1× in this paper · explained in 2 episodes
• Semi-supervised Learning — 1× in this paper · explained in 4 episodes
• Transferability — 1× in this paper · explained in 4 episodes
• Transformer Architecture — 1× in this paper · explained in 2 episodes

Sources

• DINOv3
• Parameter-Efficient Fine-Tuning for Pre-Trained Vision Models: A Survey and Benchmark
• Parameter-Efficient Fine-Tuning for Foundation Models
• LoRA-FA: Efficient and Effective Low Rank Representation Fine-tuning
• RoBERTa: A Robustly Optimized BERT Pretraining Approach
• EVA-CLIP: Improved Training Techniques for CLIP at Scale

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